no code implementations • 1 Apr 2024 • Anumanchi Agastya Sai Ram Likhit, Divyansh Tripathi, Akshay Agarwal
This paper introduces a novel sector-based methodology for star-galaxy classification, leveraging the latest Sloan Digital Sky Survey data (SDSS-DR18).
no code implementations • 15 Sep 2021 • Aayushi Agarwal, Akshay Agarwal, Sayan Sinha, Mayank Vatsa, Richa Singh
MD-CSDNetwork is a novel cross-stitched network with two parallel branches carrying the spatial and frequency information, respectively.
no code implementations • 5 Jan 2021 • Akshay Agarwal, Shashank Maiya, Sonu Aggarwal
Customer service is a setting that calls for empathy in live human agent responses.
no code implementations • 29 Oct 2020 • Divyam Anshumaan, Akshay Agarwal, Mayank Vatsa, Richa Singh
Experiments are performed using multiple databases and CNN models to establish the effectiveness of the proposed WaveTransform attack and analyze the importance of a particular frequency component.
no code implementations • 25 Oct 2020 • Saheb Chhabra, Akshay Agarwal, Richa Singh, Mayank Vatsa
However, the lack of generalizability of existing defense algorithms and the high variability in the performance of the attack algorithms for different databases raises several questions on the effectiveness of the defense algorithms.
1 code implementation • 25 Oct 2020 • Nilay Sanghvi, Sushant Kumar Singh, Akshay Agarwal, Mayank Vatsa, Richa Singh
The major problem with existing work is the generalizability against multiple attacks both in the seen and unseen setting.
no code implementations • 25 Oct 2020 • Mehak Gupta, Vishal Singh, Akshay Agarwal, Mayank Vatsa, Richa Singh
Presentation attacks are posing major challenges to most of the biometric modalities.
no code implementations • 7 Feb 2020 • Richa Singh, Akshay Agarwal, Maneet Singh, Shruti Nagpal, Mayank Vatsa
Face recognition algorithms have demonstrated very high recognition performance, suggesting suitability for real world applications.
no code implementations • 22 Feb 2018 • Gaurav Goswami, Nalini Ratha, Akshay Agarwal, Richa Singh, Mayank Vatsa
In this paper, we attempt to unravel three aspects related to the robustness of DNNs for face recognition: (i) assessing the impact of deep architectures for face recognition in terms of vulnerabilities to attacks inspired by commonly observed distortions in the real world that are well handled by shallow learning methods along with learning based adversaries; (ii) detecting the singularities by characterizing abnormal filter response behavior in the hidden layers of deep networks; and (iii) making corrections to the processing pipeline to alleviate the problem.